Enhancing Convergence in Federated Learning: A Contribution Towards Reliable AI Systems

The Convergence of AI and Precision Medicine

The convergence of artificial intelligence (AI) and precision medicine promises to revolutionize healthcare. Precision medicine methods identify unique patient phenotypes and healthcare needs, while AI leverages sophisticated computation and inference to generate insights, enable systems to reason and learn, and empower clinicians through augmented intelligence.

Recent research suggests that exploring this convergence will help solve the most challenging problems facing precision medicine, where non-genomic and genomic determinants, combined with patient symptoms, clinical history, and lifestyle data, can facilitate personalized diagnosis and prognosis.

The National Academy of Medicine has recognized the “unprecedented opportunities” for AI to augment the work of specialists and combat the realities of human limitations, such as fatigue and inattention. While care must be taken with the use of these technologies, great promise exists in their responsible application.

Fueling the Transformation: Data Digitization and Technology Uptake

The digitization of health-related data and the rapid adoption of technology are driving the development and use of AI in healthcare. However, multimodal data integration, security, federated learning (which requires advances in privacy, large-scale machine learning, and distributed optimization), model performance, and bias pose challenges to the effective use of AI in healthcare.

Three main principles for successful AI adoption in healthcare include:

1. Data and Security: Ensuring full transparency and trust in how AI systems are trained and the data/knowledge used to train them. As humans and AI systems increasingly work together, trust in the output of these systems is essential.

2. Analytics and Insights: Enabling “augmented intelligence” and “actionable insights” that support what humans do, not replace them. AI can combine input from multiple sources, reason at a semantic level, and assist healthcare professionals in making more informed decisions.

3. Shared Expertise: Recognizing the complementary relationship between AI systems and human professionals, leading to workforce change and the development of new skills. Skilled experts with access to the latest hardware are required to create cutting-edge AI models and build high-quality business applications.

The Impact of Big Data and AI Across Healthcare

The phenomenon of big data, characterized by the five Vs (volume, velocity, variety, veracity, and value), is contributing to the increasing complexity of data management and workloads, creating a greater need for advanced analytics to discover insights. Mobile devices have made technology more consumable, driving user demand for interactive visual analytics tools.

Big data analytics and AI are becoming omnipresent across the entire healthcare spectrum, including payers, providers, policymakers/government, patients, and product manufacturers. AI-based tools are used to mitigate fraud, waste, and abuse in payer programs, detect adverse events, predict patient readmission risk, control and predict infections and outbreaks, and transform health and healthcare for socially disadvantaged groups.

The Rise of Precision Medicine

Precision medicine aims to personalize care for every individual, requiring access to massive amounts of data, such as data collected through initiatives like the UK Biobank and the All of Us project, coupled with a receptive healthcare ecosystem willing to abandon the conventional approach to care in favor of a more highly individualized strategy.

The past decade has seen remarkable growth and acceptance of AI in healthcare, with AI technologies recognizing sophisticated patterns and hidden structures to perform as well as or better than clinicians in image-based detection and diagnostic systems. AI-enabled clinical decision-support systems can reduce diagnostic errors, augment intelligence to support decision-making, and assist clinicians with EHR data extraction and documentation tasks.

The term “augmented intelligence” may be a more apt description of the future interplay between data, computation, and healthcare providers, where AI systems exhibit four main characteristics: understanding, reasoning, learning, and empowering. This aligns with Friedman’s fundamental theorem of biomedical informatics, which posits that “the healthcare system with AI will be better than the healthcare system without it.”

Precision Medicine in Action

Precision medicine offers healthcare providers the ability to discover and present information that either validates or alters the trajectory of a medical decision, facilitating the delivery of personalized care for each patient. Advances in precision medicine manifest into tangible benefits, such as early disease detection and personalized treatment design.

Some key examples of the convergence between AI and precision medicine include:

1. Genome-Informed Prescribing: AI techniques have proven useful for efficient and high-throughput genome interpretation, enabling the personalization of medications and dosages based on a patient’s genotype.

2. Radiogenomics: Establishing associations between cancer imaging features and gene expression using AI can predict a patient’s risk of developing toxicity following radiotherapy, informing personalized treatment plans.

3. Predicting Treatment Response: AI demonstrates potential applications in using gene expression data to predict a patient’s response to chemotherapy, assisting clinicians in selecting the most effective treatment.

4. Incorporating Environmental Considerations: AI can augment diagnostic capabilities in resource-poor locations, translating into better patient classification and more personalized therapy planning, considering factors such as homelessness, transportation, and remote expertise availability.

5. Risk Prediction and Early Detection: AI-enabled recognition of phenotype features through EHR or images, combined with genetic data, can allow faster genetic disease diagnosis, while automated speech analytics and smartphone sensor data can provide indicators for early-stage assessment of conditions like dementia and Parkinson’s disease.

Challenges and Considerations

While AI and precision medicine demonstrate great promise, researchers face challenges in adopting unified data formats, obtaining sufficient and high-quality labeled data for training algorithms, and addressing regulatory, privacy, and sociocultural requirements.

Three main challenges that impact the successful transition of AI systems to real-world healthcare applications are:

1. Fairness and Bias: Health data can be biased during dataset building and processing, leading to AI models amplifying the bias and making unfavorable decisions. Techniques to detect and mitigate bias, such as improving data diversity and leveraging fairness-enhancing algorithms, are crucial.

2. Socio-Environmental Factors: The environmental factors and workflows where the AI model would be deployed may impact model performance and clinical efficacy. Validating AI models in the clinical environment and considering an iteration loop to incorporate user feedback are essential.

3. Data Safety and Privacy: As AI and precision medicine converge, the collection and integration of sensitive personal data (e.g., genomics, medical history, behaviors, social data) raise concerns about data privacy and safety. Building a secure and well-controlled ecosystem for data management and sharing is crucial, requiring new technology adoptions, collaborations, and the creation of new regulations and business models.

Conclusion: Towards Reliable and Personalized AI-Powered Healthcare

The synergy between AI and precision medicine aligns with the ultimate goal of disease prevention and early detection, which could ultimately decrease the disease burden for the public and reduce the cost of preventable healthcare. However, addressing the challenges of fairness, socio-environmental factors, and data privacy is essential for the successful and reliable integration of these transformative technologies into the healthcare system.

By overcoming these obstacles, the convergence of AI and precision medicine promises to revolutionize healthcare, empowering medical professionals and consumers with highly personalized diagnostic and therapeutic information to improve patient outcomes and overall population health.